Multi-objective optimization of IPMSM for electric vehicles based on the combinatorial surrogate model and the hierarchical design method

Abstract

This paper investigates the optimization design of interior permanent magnet synchronous motors (IPMSM) for electric vehicles (EVs). The optimization process of IPMSM involves numerous design parameters, and the optimization objectives often conflict with each other, resulting in a vast design space and difficulties in establishing an accurate mathematical model. The traditional finite element analysis (FEA) optimization methods are time-consuming and computationally intensive, posing a significant challenge to achieving high-performance IPMSM with high torque, high efficiency, low vibration, and low losses. To address this issue, this paper proposes a multi-objective optimization of IPMSM for electric vehicles based on the combinatorial surrogate model and the hierarchical design method. Firstly, a comprehensive sensitivity coefficient method is employed to categorize design variables into two layers: high-sensitivity design variables (HSDVs) and low-sensitivity design variables (LSDVs). Secondly, using the improved Latin hypercube sampling (LHS) method to extract sample data, a high-precision combined surrogate model (RSM + Kriging) is constructed and combined with the non-dominated sorting genetic algorithm II (NSGA-II) optimization algorithm to optimize HSDVs. Meanwhile, the fuzzy inference Taguchi method (FITM) is utilized to optimize the LSDVs. Finally, the performance of the IPMSM before and after optimization has been analyzed through the FEA method, and different optimization methods were introduced for comparison. The results show that compared to other optimization methods, the optimization approach proposed in this paper can effectively enhance the overall performance of the IPMSM. The average torque of the optimized IPMSM increased by 5.22 %, the torque ripple decreased by 77.64 %, and the total losses were reduced by 6.21 %. Furthermore, compared to the traditional FEA method, this method reduces optimization time and improves optimization efficiency without compromising on optimization accuracy.